Gender: Male 
Age: 25-34
Nationality: United Kingdom
Ethnicity: White / Caucasian 
Marital status: Single 
First language: English 
Religion: Christian 
Education: Graduate degree
Mother's education: Graduate degree
Father's education: Doctorate or above
Self-assessed socioeconomic status (1-10): 8
Home: Rent 
Employment: Employed full time
Occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Mother's occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Father's occupation: Manager or business owner (those who plan, direct, coordinate and evaluate the overall activities of enterprises, governments and other organizations)
Hobbies: Listen to jazz;Listen to rock/indie music;Go to gigs;Exercise;Use social media;Visit to stately homes;Attend football matches;Watch sports;Listen to classical music 
Devices used daily: Laptop;Smartphone;Smartwatch 
NLP tools known: Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Grammar checker (i.e., correcting grammar mistakes);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools used: Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Grammar checker (i.e., correcting grammar mistakes);Spell checker (i.e., correcting misspellings);Speech-to-Text (i.e. a computer transcribing your spoken language to written language) 
NLP tools they would find useful: Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Grammar checker (i.e., correcting grammar mistakes);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Text-to-Speech (i.e, computers being able to read out loud written language) 
Frequency of LLM use: Nearly every day
LLMs used: ChatGPT;GitHub Copilot;Google Gemini;Microsoft Bing AI 
LLM use cases: Translation;Coding;Brainstorming;Analyze data;Writing (e.g., composing emails);Generate art;Completing assignments;Get information about current events;Answering questions about general knowledge;Collecting references;Generic chatbot conversation 
Contexts of LLM use: Work;School/University;Personal;Creative or artistic;Learning;Entertainment 
Prompt 1: Inventions
Prompt 2: Artificial intelligence
Prompt 3: Cryptocurrency
Prompt 4: Donald Tump
Prompt 5: Elon Musk
Prompt 6: Tesla
Prompt 7: Space X
Prompt 8: Ozone layer
Prompt 9: Longevity
Prompt 10: JD Vance
Comments: Great survey and I love it.